Choice set variability and contextual heterogeneity in work trip mode choice in Chennai city

Abstract

This paper focuses on systematic behavioral heterogeneity in mode choice decisions of working population in Chennai city. The sources of heterogeneity that are investigated include (a) variation in choice set across decision makers, (b) differences in innate preferences and responsiveness to explanatory factors due to variations in their degree of captivity to non-personal modes and number of co-passengers during work commute. Existing studies that account for such systematic differences among decision-maker in a developing country context capture only the preference heterogeneity, impose unrealistic restrictions on the utility of non-personal modes (say for e.g. for joint (copassengers > 0) trips) and specify choice sets to be fixed and identical across decision-makers. Addressing these issues, the main objectives of the study were to capture the effect of factors influencing perceived availability of alternatives in the choice set; behavioral comparison of alternate choice set representations namely: fixed choice set, choice set with explicit specification of unavailability of alternatives, partial and probabilistic choice set; investigate potential heterogeneity in innate preference to alternatives and responsiveness to explanatory factors due to difference in captivity status and the presence of co-passengers; and examine whether and how these heterogeneity effects differ based on alternative choice set representations. The effect of factors influencing the perceived availability of alternatives is captured using a set of binary logit models. Unlike many studies, where captivity is represented by a binary variable, behavioral differences across three levels of captivity (captive by vehicle ownership, captive by driving knowledge, and semi-captive) are investigated. The results from the empirical analysis show that disregarding preference or response heterogeneity, when present, can lead to poorer goodness-of-fit in models. It also shows that partial and probabilistic choice set representation is more behaviorally consistent than explicit specification of alternative unavailability, which in turn is better than assuming choice set to be identical across decision-makers.